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Hierarchical Representations for Spatio-Temporal Visual Attention Modeling and Understanding. (arXiv:2308.05189v1 [cs.CV])
This PhD. Thesis concerns the study and development of hierarchical
representations for spatio-temporal visual attention modeling and understanding
in video sequences. More specifically, we propose two computational models for
visual attention. First, we present a generative probabilistic model for
context-aware visual attention modeling and understanding. Secondly, we develop
a deep network architecture for visual attention modeling, which first
estimates top-down spatio-temporal visual attention, and ultimately serves for
modeling attention in the temporal domain.
( 2
min )
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. (arXiv:2211.03942v3 [cs.LG] UPDATED)
In private federated learning (FL), a server aggregates differentially
private updates from a large number of clients in order to train a machine
learning model. The main challenge in this setting is balancing privacy with
both classification accuracy of the learnt model as well as the number of bits
communicated between the clients and server. Prior work has achieved a good
trade-off by designing a privacy-aware compression mechanism, called the
minimum variance unbiased (MVU) mechanism, that numerically solves an
optimization problem to determine the parameters of the mechanism. This paper
builds upon it by introducing a new interpolation procedure in the numerical
design process that allows for a far more efficient privacy analysis. The
result is the new Interpolated MVU mechanism that is more scalable, has a
better privacy-utility trade-off, and provides SOTA results on
communication-efficient private FL on a variety of datasets.
( 2
min )
Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance. (arXiv:2308.05619v1 [stat.ML])
As data shift or new data become available, updating clinical machine
learning models may be necessary to maintain or improve performance over time.
However, updating a model can introduce compatibility issues when the behavior
of the updated model does not align with user expectations, resulting in poor
user-model team performance. Existing compatibility measures depend on model
decision thresholds, limiting their applicability in settings where models are
used to generate rankings based on estimated risk. To address this limitation,
we propose a novel rank-based compatibility measure, $C^R$, and a new loss
function that aims to optimize discriminative performance while encouraging
good compatibility. Applied to a case study in mortality risk stratification
leveraging data from MIMIC, our approach yields more compatible models while
maintaining discriminative performance compared to existing model selection
techniques, with an increase in $C^R$ of $0.019$ ($95\%$ confidence interval:
$0.005$, $0.035$). This work provides new tools to analyze and update risk
stratification models used in clinical care.
( 2
min )
Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models. (arXiv:2308.05237v1 [quant-ph])
In this research, a comparative study of four Quantum Machine Learning (QML)
models was conducted for fraud detection in finance. We proved that the Quantum
Support Vector Classifier model achieved the highest performance, with F1
scores of 0.98 for fraud and non-fraud classes. Other models like the
Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and
Sampler QNN demonstrate promising results, propelling the potential of QML
classification for financial applications. While they exhibit certain
limitations, the insights attained pave the way for future enhancements and
optimisation strategies. However, challenges exist, including the need for more
efficient Quantum algorithms and larger and more complex datasets. The article
provides solutions to overcome current limitations and contributes new insights
to the field of Quantum Machine Learning in fraud detection, with important
implications for its future development.
( 2
min )
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke. (arXiv:2308.05110v1 [cs.LG])
Stroke is a significant cause of mortality and morbidity, necessitating early
predictive strategies to minimize risks. Traditional methods for evaluating
patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II,
IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy
and interpretability. This paper proposes a novel approach: an interpretable,
attention-based transformer model for early stroke mortality prediction. This
model seeks to address the limitations of previous predictive models, providing
both interpretability (providing clear, understandable explanations of the
model) and fidelity (giving a truthful explanation of the model's dynamics from
input to output). Furthermore, the study explores and compares fidelity and
interpretability scores using Shapley values and attention-based scores to
improve model explainability. The research objectives include designing an
interpretable attention-based transformer model, evaluating its performance
compared to existing models, and providing feature importance derived from the
model.
( 2
min )
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures. (arXiv:2308.05106v1 [cs.CV])
This paper presents an investigation into machine learning techniques for
violence detection in videos and their adaptation to a federated learning
context. The study includes experiments with spatio-temporal features extracted
from benchmark video datasets, comparison of different methods, and proposal of
a modified version of the "Flow-Gated" architecture called "Diff-Gated."
Additionally, various machine learning techniques, including super-convergence
and transfer learning, are explored, and a method for adapting centralized
datasets to a federated learning context is developed. The research achieves
better accuracy results compared to state-of-the-art models by training the
best violence detection model in a federated learning context.
( 2
min )
Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries. (arXiv:2308.05118v1 [q-bio.GN])
This paper demonstrates the utility of organized numerical representations of
genes in research involving flat string gene formats (i.e., FASTA/FASTQ5).
FASTA/FASTQ files have several current limitations, such as their large file
sizes, slow processing speeds for mapping and alignment, and contextual
dependencies. These challenges significantly hinder investigations and tasks
that involve finding similar sequences. The solution lies in transforming
sequences into an alternative representation that facilitates easier clustering
into similar groups compared to the raw sequences themselves. By assigning a
unique vector embedding to each short sequence, it is possible to more
efficiently cluster and improve upon compression performance for the string
representations of cDNA libraries. Furthermore, through learning alternative
coordinate vector embeddings based on the contexts of codon triplets, we can
demonstrate clustering based on amino acid properties. Finally, using this
sequence embedding method to encode barcodes and cDNA sequences, we can improve
the time complexity of the similarity search by coupling vector embeddings with
an algorithm that determines the proximity of vectors in Euclidean space; this
allows us to perform sequence similarity searches in a quicker and more modular
fashion.
( 2
min )
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